BeeBot

BeeBot routes incoming requests to specialized task handlers through an LLM-based decision layer that analyzes task intent and selects appropriate execution paths. The system maintains a registry of task types and uses language model reasoning to decompose complex requests into sequential or parallel subtasks, with built-in error handling and fallback mechanisms for failed task execution.

BeeBot provides a sandboxed execution environment for running generated or user-provided code snippets with resource isolation and timeout enforcement. The system integrates with code generation models to produce executable code and validates syntax before execution, capturing stdout/stderr and execution results for downstream task handlers.

BeeBot profiles task execution performance (latency, memory usage, handler selection frequency) and generates optimization recommendations based on observed patterns. The system identifies slow handlers, inefficient routing decisions, and bottlenecks in task chains, providing actionable suggestions (switch to faster provider, cache results, parallelize tasks). Profiling data is collected continuously with minimal overhead and can be exported for analysis.

BeeBot implements a plugin architecture where task handlers are registered at runtime through a handler registry interface. Handlers expose metadata (name, description, input schema, output schema) that the routing layer uses to match incoming requests, enabling extensibility without modifying core framework code. The system supports both synchronous and asynchronous handlers with automatic execution model detection.

BeeBot abstracts multiple LLM providers (OpenAI, Anthropic, local Ollama) behind a unified interface, allowing requests to be routed to different models based on cost, latency, or availability constraints. The system implements fallback chains where if one provider fails or times out, requests automatically retry against alternative providers with configurable backoff strategies.

BeeBot validates task handler outputs against declared output schemas (JSON Schema, Pydantic models) before returning results to downstream consumers. The validation layer catches malformed outputs early, provides detailed error messages about schema violations, and can optionally coerce or transform outputs to match expected schemas using configurable validators.

BeeBot captures detailed execution traces for each task including routing decisions, handler selection, input/output data, execution duration, and error information. Traces are structured as JSON and can be exported to observability platforms (Datadog, New Relic, custom backends) for monitoring and debugging. The system includes built-in metrics collection for latency, error rates, and handler performance.

BeeBot supports conditional execution paths where task results determine which subsequent tasks execute. The system evaluates conditions (based on task output, error status, or explicit predicates) and branches execution to different handlers, enabling complex workflows like error recovery, A/B testing, or multi-path processing. Branching logic is declarative and can be composed with sequential and parallel task chains.

BeeBot executes multiple independent tasks concurrently and aggregates their results using configurable merge strategies. The system manages thread/async pools, handles partial failures (some tasks succeed while others fail), and provides result aggregation functions (collect all, wait for first, merge dictionaries). Execution is non-blocking and supports both CPU-bound and I/O-bound tasks through async/await patterns.

BeeBot persists task execution state (intermediate results, execution progress, handler selections) to enable resumption after failures or interruptions. The system checkpoints state at configurable intervals and can restore execution from the last checkpoint, skipping already-completed tasks. State is stored in pluggable backends (file system, database, Redis) and includes metadata for debugging and audit trails.

BeeBot pauses task execution at designated checkpoints and requests human approval before proceeding. The system presents task context (input, proposed output, routing decision) to human reviewers through a pluggable interface (web UI, Slack, email) and waits for approval/rejection/modification. Approved tasks resume execution; rejected tasks trigger error handlers or alternative paths.